[](https://github.com/LuxDL/Lux.jl/discussions)
[](http://lux.csail.mit.edu/dev/)
[](http://lux.csail.mit.edu/stable/)
[](https://github.com/LuxDL/Lux.jl/actions/workflows/CI.yml)
[&logo=github)](https://github.com/LuxDL/Lux.jl/actions/workflows/CIPreRelease.yml)
[](https://buildkite.com/julialang/lux-dot-jl)
[](https://codecov.io/gh/LuxDL/Lux.jl)
[](https://lux.csail.mit.edu/benchmarks/)
[](https://juliapkgstats.com/pkg/Lux)
[](https://juliapkgstats.com/pkg/Lux)
[](https://github.com/aviatesk/JET.jl)
[](https://github.com/JuliaTesting/Aqua.jl)
[](https://github.com/SciML/ColPrac)
[](https://github.com/SciML/SciMLStyle)
Elegant & Performant Scientific Machine Learning in Julia
A Pure Julia Deep Learning Framework designed for Scientific Machine Learning
## ๐ป Installation
```julia
import Pkg
Pkg.add("Lux")
```
## ๐คธ Quickstart
```julia
using Lux, Random, Optimisers, Zygote
# using LuxCUDA, AMDGPU, Metal, oneAPI # Optional packages for GPU support
# Seeding
rng = Xoshiro(0)
# Construct the layer
model = Chain(BatchNorm(128), Dense(128, 256, tanh), BatchNorm(256),
Chain(Dense(256, 1, tanh), Dense(1, 10)))
# Get the device determined by Lux
device = gpu_device()
# Parameter and State Variables
ps, st = Lux.setup(rng, model) .|> device
# Dummy Input
x = rand(rng, Float32, 128, 2) |> device
# Run the model
y, st = Lux.apply(model, x, ps, st)
# Gradients
gs = only(gradient(p -> sum(first(Lux.apply(model, x, p, st))), ps))
# Optimization
st_opt = Optimisers.setup(Optimisers.Adam(0.0001), ps)
st_opt, ps = Optimisers.update(st_opt, ps, gs)
```
## ๐ Examples
Look in the [examples](/examples/) directory for self-contained usage examples. The [documentation](https://lux.csail.mit.edu) has examples sorted into proper categories.
## ๐งช Testing
The full test of `Lux.jl` takes a long time, here's how to test a portion of the code.
For each `@testitem`, there are corresponding `tags`, for example:
```julia
@testitem "SkipConnection" setup=[SharedTestSetup] tags=[:core_layers]
```
For example, let's consider the tests for `SkipConnection`:
```julia
@testitem "SkipConnection" setup=[SharedTestSetup] tags=[:core_layers] begin
...
end
```
We can test the group to which `SkipConnection` belongs by testing `core_layers`.
To do so set the `LUX_TEST_GROUP` environment variable, or rename the tag to
further narrow the test scope:
```shell
export LUX_TEST_GROUP="core_layers"
```
Or directly modify the default test tag in `runtests.jl`:
```julia
# const LUX_TEST_GROUP = lowercase(get(ENV, "LUX_TEST_GROUP", "all"))
const LUX_TEST_GROUP = lowercase(get(ENV, "LUX_TEST_GROUP", "core_layers"))
```
But be sure to restore the default value "all" before submitting the code.
Furthermore if you want to run a specific test based on the name of the testset, you can
use [TestEnv.jl](https://github.com/JuliaTesting/TestEnv.jl) as follows. Start with activating the Lux environment and then run the following:
```julia
using TestEnv; TestEnv.activate(); using ReTestItems;
# Assuming you are in the main directory of Lux
ReTestItems.runtests("tests/"; name = "NAME OF THE TEST")
```
For the `SkipConnection` tests that would be:
```julia
ReTestItems.runtests("tests/"; name = SkipConnection)
```
## ๐ Getting Help
For usage related questions, please use [Github Discussions](https://github.com/orgs/LuxDL/discussions) which allows questions and answers to be indexed. To report bugs use [github issues](https://github.com/LuxDL/Lux.jl/issues) or even better send in a [pull request](https://github.com/LuxDL/Lux.jl/pulls).
## ๐งโ๐ฌ Citation
If you found this library to be useful in academic work, then please cite:
```bibtex
@software{pal2023lux,
author = {Pal, Avik},
title = {{Lux: Explicit Parameterization of Deep Neural Networks in Julia}},
month = apr,
year = 2023,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {v0.5.0},
doi = {10.5281/zenodo.7808904},
url = {https://doi.org/10.5281/zenodo.7808904}
}
@thesis{pal2023efficient,
title = {{On Efficient Training \& Inference of Neural Differential Equations}},
author = {Pal, Avik},
year = {2023},
school = {Massachusetts Institute of Technology}
}
```
Also consider starring [our github repo](https://github.com/LuxDL/Lux.jl/).